Li Zhongwen, Xie He, Wang Zhouqian, Li Daoyuan, Chen Kuan, Zong Xihang, Qiang Wei, Wen Feng, Deng Zhihong, Chen Limin, Li Huiping, Dong He, Wu Pengcheng, Sun Tao, Cheng Yan, Yang Yanning, Xue Jinsong, Zheng Qinxiang, Jiang Jiewei, Chen Wei
Ningbo Key Laboratory of Medical Research on Blinding Eye Diseases, Ningbo Eye Institute, Ningbo Eye Hospital, Wenzhou Medical University, Ningbo, 315000, China.
National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, China.
NPJ Digit Med. 2024 Jul 6;7(1):181. doi: 10.1038/s41746-024-01174-w.
The main cause of corneal blindness worldwide is keratitis, especially the infectious form caused by bacteria, fungi, viruses, and Acanthamoeba. The key to effective management of infectious keratitis hinges on prompt and precise diagnosis. Nevertheless, the current gold standard, such as cultures of corneal scrapings, remains time-consuming and frequently yields false-negative results. Here, using 23,055 slit-lamp images collected from 12 clinical centers nationwide, this study constructed a clinically feasible deep learning system, DeepIK, that could emulate the diagnostic process of a human expert to identify and differentiate bacterial, fungal, viral, amebic, and noninfectious keratitis. DeepIK exhibited remarkable performance in internal, external, and prospective datasets (all areas under the receiver operating characteristic curves > 0.96) and outperformed three other state-of-the-art algorithms (DenseNet121, InceptionResNetV2, and Swin-Transformer). Our study indicates that DeepIK possesses the capability to assist ophthalmologists in accurately and swiftly identifying various infectious keratitis types from slit-lamp images, thereby facilitating timely and targeted treatment.
全球角膜盲的主要原因是角膜炎，尤其是由细菌、真菌、病毒和棘阿米巴引起的感染性角膜炎。有效管理感染性角膜炎的关键在于及时、准确的诊断。然而，目前的金标准，如角膜刮片培养，仍然耗时且经常产生假阴性结果。在此，本研究利用从全国12个临床中心收集的23055张裂隙灯图像，构建了一个临床可行的深度学习系统DeepIK，该系统可以模拟人类专家的诊断过程，以识别和区分细菌性、真菌性、病毒性、阿米巴性和非感染性角膜炎。DeepIK在内部、外部和前瞻性数据集中表现出卓越的性能（所有受试者工作特征曲线下面积>0.96），并且优于其他三种最先进的算法（DenseNet121、InceptionResNetV2和Swin-Transformer）。我们的研究表明，DeepIK有能力协助眼科医生从裂隙灯图像中准确、快速地识别各种感染性角膜炎类型，从而促进及时、有针对性的治疗。
